10 differences between amateurs and professional analysts
10 differences between amateurs and professional analysts
The journey to becoming a "real" data analyst
Today’s newsletter is all about celebrating the badass expert data analysts among us!
There are some big differences between an amateur and an expert professional analyst and in this newsletter, I’ll introduce 10 of them to you, along with links to blog posts where I cover each one in detail.
Data pro vs amateur difference #1 — Software skills
Contrary to popular belief, expert analysts are code-first data professionals, thumbing their noses at point-and-click analytics interfaces like MS Excel. Learn more.
Data pro vs amateur difference #2 — Handling lots of data with ease
Expert analysts are undaunted by dataset size, which often means they pick up data engineering skills as part of their job. Learn more.
Data pro vs amateur difference #3 — Immunity to data science bias
Another big difference between an amateur and an expert analyst is that the expert has developed an all-encompassing disrespect for data. They never pronounce data with a capital ‘D’ because they know why it’s dangerous to put data on a pedestal. Learn more.
Data pro vs amateur difference #4 — Understanding the career
Unlike amateurs, the professional analyst is an analyst by choice, not by misfortune. To them, analytics is a discipline of excellence in its own right, not a stepping stone to some other profession (like machine learning or statistics). Learn more.
Data pro vs amateur difference #5 — Refusing to be a data charlatan
Professional analysts refuse to be data charlatans: peddlers of toxic hindsight. To avoid accidentally becoming a data charlatan, learn more here.
Data pro vs amateur difference #6 — Resistance to confirmation bias
Confirmation bias sucks all the value out of data analysis, so professional analysts work to build up their resistance to it. Learn how.
Data pro vs amateur difference #7 — Realistic expectations of data
Among all data professionals, analysts are the ones who spend the most time wading through ugly, messy data. Expert analysts have seen it all and they’re no longer shocked by real-world data - they’re painfully aware that you often need to start with bad data to figure out how to make better data… and they’re the ones tasked with the figuring. Learn more.
Data pro vs amateur difference #8 — Knowing how to add value
Expert analysts understand why proactive analytics is the most valuable contribution they can make and they know why starting anywhere except with your decision-maker’s needs and priorities is bound to lead you astray. Learn more.
Data pro vs amateur difference #9 — Thinking differently about time
Analytics is an investment of time… and like all investments, there’s a chance it leaves you empty-handed. Expert analysts understand this in their bones and work hard to optimize their time return-on-investment (ROI) in a ways that amateurs may find surprising. Learn more.
Data pro vs amateur difference #10 — Nuanced view of excellence
The analytics game is all about optimizing inspiration-per-minute. Unlike amateurs, expert analysts don’t view speed as a dirty word but rather as a nuanced concept that guides how they think of their work, how they prioritize, how they assess performance, and how they develop their skills. Learn more.
